Project Summary/Abstract The volume of published evidence in biomedicine is growing at a rapid pace. Doctors and researchers must keep pace with the rapid generation of new evidence to provide up-to-date care. The review and aggregation of knowledge is a time-intensive process that must be repeated to assimilate the latest evidence. Furthermore, published evaluations of evidence (e.g., literature reviews, meta-analyses, systematic reviews) are not in a format that is able to leverage modern data analytics. Charles River Analytics proposes to design and demonstrate a toolkit for Capturing Health Information from Research Ontologies (CHIRON). CHIRON uses human-in-the-loop approaches to build models of research designs and attributes based on an ontological framework. It leverages a Systemic Functional Grammar (SFG) toolkit for rapidly capturing model data and intuitive user interfaces ensuring accuracy. The format of the research models facilitates their sharing and reuse. This sharable format enables doctors and researchers to rapidly aggregate models and assimilate new representations of evidence into an existing body of research using semantic queries. Furthermore, they facilitate qualitative analyses to obtain summaries and insights about the aggregated literature. The proposed workflow, encompassing capture, analysis, and visualization of research models will decrease the amount of time required to find and aggregate relevant research.